Monitoring fatigue in sport is critical to achieve elite performance and may benefit from machine learning techniques that are liable to predict changes in fatigue state. In this paper we present and compare different machine learning models to predict the Rate of Perceived Exertion (RPE) of training or game sessions for professional football (soccer) players. We compare different approaches to train predictive models in a supervised setting (regression) with a focus on individualized and group-based approaches, i.e. training a specific model for each player or predefined groups of players (full team or clusters defined using unsupervised learning). Both player-informed and player-agnostic models are compared in the group-based approach, i.e. providing or not player id as feature during training and inference. Compared models have been trained on real data collected during a full season of professional football players, and using among others, anthropometric, running activity, heart rate and weather data. The best results are obtained using a player-informed team-based approach with a Random Forest regressor (0.793 MAE, 1.033 RMSE). Results obtained are competitive with the best reported in the literature for this predictive task in elite Football players.

International audience

Document Indexing is but not limited to summarizing document contents with a small set of keywords or concepts of a knowledge base. Such a compact representation of document contents eases their use in numerous processes such as content-based information retrieval, corpus-mining and classification. An important effort has been devoted in recent years to (partly) automate semantic indexing, i.e. associating concepts to documents, leading to the availability of large corpora of semantically indexed documents. In this paper we introduce a method that hierarchically clusters documents based on their semantic indices while providing the proposed clusters with semantic labels . Our approach follows a neighbor joining strategy. Starting from a distance matrix reflecting the semantic similarity of documents, it iteratively selects the two closest clusters to merge them in a larger one. The similarity matrix is then updated. This is usually done by combining similarity of the two merged clusters, e.g. using the average similarity. We propose in this paper an alternative approach where the new cluster is first semantically annotated and the similarity matrix is then updated using the semantic similarity of this new annotation with those of the remaining clusters. The hierarchical clustering so obtained is a binary tree with branch lengths that convey semantic distances of clusters. It is then post-processed by using the branch lengths to keep only the most relevant clusters. Such a tool has numerous practical applications as it automates the organization of documents in meaningful clusters (e.g. papers indexed by MeSH terms, bookmarks or pictures indexed by WordNet) which is a tedious everyday task for many people. We assess the quality of the proposed methods using a specific benchmark of annotated clusters of bookmarks that were built manually. Each dataset of this benchmark has been clustered independently by several users. Remarkably, the clusters automatically built by our method are congruent with the clusters proposed by experts. All resources of this work, including source code, jar file, benchmark files and results are available at this address: http://sc.nicolasfiorini.info .

Ontologies are core elements of numerous applications that are based on computer-processable expert knowledge. They can be used to estimate the Information Content (IC) of the key concepts of a domain: a central notion on which depend various ontology-driven analyses, e.g. semantic measures. This paper proposes new IC models based on the belief functions theoretical framework. These models overcome limitations of existing ICs that do not consider the inductive inference assumption intuitively assumed by human operators, i.e. that occurrences of a concept (e.g. Maths) not only impact the IC of more general concepts (e.g. Sciences), as considered by traditional IC models, but also the one of more specific concepts (e.g. Algebra). Interestingly, empirical evaluations show that, in addition to modelling the aforementioned assumption, proposed IC models compete with best state-of-the-art models in several evaluation settings.

UNLABELLED: The semantic measures library and toolkit are robust open-source and easy to use software solutions dedicated to semantic measures. They can be used for large-scale computations and analyses of semantic similarities between terms/concepts defined in terminologies and ontologies. The comparison of entities (e.g. genes) annotated by concepts is also supported. A large collection of measures is available. Not limited to a specific application context, the library and the toolkit can be used with various controlled vocabularies and ontology specifications (e.g. Open Biomedical Ontology, Resource Description Framework). The project targets both designers and practitioners of semantic measures providing a JAVA library, as well as a command-line tool that can be used on personal computers or computer clusters. AVAILABILITY AND IMPLEMENTATION: Downloads, documentation, tutorials, evaluation and support are available at http://www.semantic-measures-library.org.